code: def transformers_hyperparameter_trainer(tmp_path):
      random.seed(555)
      np.random.seed(555)
      torch.manual_seed(555)
  
      tokenizer = DistilBertTokenizerFast.from_pretrained(
          "distilbert-base-uncased"
      )
      model = DistilBertForSequenceClassification.from_pretrained(
          "distilbert-base-uncased", num_labels=2
      )
  
      train_texts = ["I love this product!", "This is terrible."]
      train_labels = [1, 0]
  
      train_encodings = tokenizer(
          train_texts, truncation=True, padding=True
      )
  
      class CustomDataset(torch.utils.data.Dataset):
          def __init__(self, encodings, labels):
              self.encodings = encodings
              self.labels = labels
  
          def __getitem__(self, idx):
              item = {
                  key: torch.tensor(val[idx])
                  for key, val in self.encodings.items()
              }
              item["labels"] = torch.tensor(self.labels[idx])
              return item
  
          def __len__(self):
              return len(self.labels)
  
      train_dataset = CustomDataset(train_encodings, train_labels)
  
      def model_init():
          return DistilBertForSequenceClassification.from_pretrained(
              "distilbert-base-uncased", num_labels=2
          )
  
      def objective(trial):
          learning_rate = trial.suggest_float(
              "learning_rate", 1e-7, 1e-1, log=True
          )
  
          training_args = TrainingArguments(
              output_dir=str(tmp_path.joinpath("results")),
              num_train_epochs=1,
              per_device_train_batch_size=4,
              learning_rate=learning_rate,
              logging_dir=str(tmp_path.joinpath("logs")),
              report_to="none",
          )
  
          trainer = Trainer(
              model_init=model_init,
              args=training_args,
              train_dataset=train_dataset,
          )
  
          train_result = trainer.train()
          return train_result.training_loss
  
      study = optuna.create_study(direction="minimize")
      study.optimize(objective, n_trials=2)
  
      best_params = study.best_params
  
      best_training_args = TrainingArguments(
          output_dir=str(tmp_path.joinpath("results")),
          num_train_epochs=3,
          per_device_train_batch_size=4,
          learning_rate=best_params["learning_rate"],
          logging_dir=str(tmp_path.joinpath("logs")),
      )
  
      return Trainer(
          model=model,
          args=best_training_args,
          train_dataset=train_dataset,
      )
  
imports: import random
  import numpy
  import optuna
  import pytest
  import sklearn
  import sklearn.cluster
  import torch
  from datasets import load_dataset
  from sentence_transformers.losses import CosineSimilarityLoss
  from setfit import SetFitModel, sample_dataset
  from setfit import Trainer
  from setfit import TrainingArguments
  from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, Trainer, TrainingArguments, pipeline
  import mlflow
label_desc: Use image node_id 4 for calling a global function with example usage: transformers_hyperparameter_trainer(tmp_path) and returns: Trainer, DistilBertForSequenceClassification, train_result, item, len